The antimicrobial effect of water extraction of Salvadora persica (Miswak) as a root canal irrigant
Bibliographic record
Abstract
The aim of this study was to evaluate the antimicrobial effect of 10% water extraction of Salvadora persica (Miswak) when used clinically as an endodontic irrigant. Twenty four uniradicular teeth with necrotic pulps were chosen. The patients were divided randomly into 2 groups: Experimental group, in which water extract of Salvadora persica (10%) was used as a root canal irrigant; and control group, in which distilled water was used as a root canal irrigant. Bacteriological samples were obtained from the canal at the step of working length determination (before the canal was subjected to instrumentation and irrigation procedures), and at the end of the biomechanical instrumentation procedures by using a sterile K–file. The file was separated from the handle using a sterile wire cutter, and the severed portion was placed in a sterile screw–capped vial containing 5 ml of thioglycollate broth as a transport media. A 0.1 ml of thioglycollate broth was inoculated on each of two brain–heart infusion agar plates: One plate was incubated under aerobic conditions, and the other was incubated under anaerobic conditions using anaerobic jar and gas pack anaerobic system. Both plates were incubated at 37 ºC for 24 hours; then, the number of bacterial colonies was counted. The results revealed that 10% water extraction of Salvadora persica is an effective antimicrobial agent when utilized clinically as an irrigant in the endodontic treatment of teeth with necrotic pulps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".